Sentiment Analysis of Political Tweets: Towards an Accurate Classifier

A series of 3-class sentiment classification experiments on a set of 2,624 tweets produced during the run-up to the Irish General Elections in February 2011, achieving the highest accuracy using supervised learning and a feature set consisting of subjectivity-lexicon-based scores, Twitter- specific features and the top 1,000 most dis- criminative words.

Abstract

We perform a series of 3-class sentiment classification experiments on a set of 2,624 tweets produced during the run-up to the Irish General Elections in February 2011. Even though tweets that have been labelled as sarcastic have been omitted from this set, it still represents a difficult test set and the highest accuracy we achieve is 61.6% using supervised learning and a feature set consisting of subjectivity-lexicon-based scores, Twitter- specific features and the top 1,000 most dis- criminative words. This is superior to various naive unsupervised approaches which use subjectivity lexicons to compute an overall sentiment score for a <tweet,political party> pair.